Statistical tests always involve a trade-off between: (a) the acceptable level of ‘false positives’ in where a non-match is inadvertently declared to be a match, and (b) the acceptable level of ‘false negatives’ where actual matches are not detected. A threshold value can be varied to make the test more restrictive or more sensitive, with the more restrictive tests increasing the risk of rejecting true positives, and the more sensitive tests increasing the risk of accepting false positives.
Perhaps the most widely discussed false positives in medical screening come from the breast cancer screening procedure mammography since more than 10% of women in the western world contract breast cancer, and the success and ease of treatment is highly dependent on early diagnosis. Mammography is the use of low-dose x-ray radiation to image the tissue inside the breast. The technique is used to screen for and diagnose breast cancer by detecting tumors or other changes in breast tissue and aids in early detection of malignant tumors, which improves chances of successful treatment. It can identify abnormalities before a lump can be felt and provides the only reliable method of locating abnormal growths in the milk ducts. Thus it may facilitate locating suspected tumors, prior to a biopsy or surgery.
In consequence of the dangers of breast cancer and the success of mammography, the guidelines laid by the U.S. Department of Health and Human Services (HHS), the American Cancer Society (ACS), the American Medical Association (AMA) and the American College of Radiology (ACR) recommend that screening mammograms be performed annually for all women over the age of 40 in good health, with annual mammograms being advisable at earlier ages for women with a family history of breast cancer or having had pror breast biopsies.
It is, of course, imperative to prevent missing a malignant tumor. To avoid unnecessary anxiety and to reduce costs of unnecessary biopsies; it is desirable to minimize false positives as well.
In mammography, the breast is compressed between two plates and exposed to X-rays. Two pictures of each breast are generally taken during a screening mammogram, with extra images from different angles being sometimes necessary for women with breast implants. With so many scans requiring analysis, it is essential to automate the analysis as much as possible and to optimize the computer aided examination of the X-Ray images, both by increased accuracy of the analysis and by faster processing times.
The size and shape of the breast is highly variable between women and the thickness of the imaged compressed tissues differs significantly between subjects. The tissue composition of the breast is also highly variable and therefore the average absorption of X-rays by the breast tissue varies significantly between women.
Compared to other anatomical regions, the breast has very low physical contrast because it is composed completely of soft tissues. In general, the breast consists of a background of fat surrounding the slightly denser, glandular structures and pathologic tissues or cysts if they are present. Typical breast calcifications are very small and thin and produce low physical contrast despite calcium being somewhat denser to X-rays than the elements from which soft tissues are constructed.
Mammography systems vary considerably and there is ongoing development work to improve the sensitivity of such equipment. Digital mammography is preferably to conventional film in that better contrast is available. Digital mammogram images are stored as digital pictures which can be transmitted easily for remote consultation.
Mammography is generally performed with a spectrum containing photons within a relatively narrow energy range (19 keV-21 kev) in an attempt to obtain high contrast with minimal dosage. The spectrum is produced using the characteristic radiation from a molybdenum anode x-ray tube and filtered by either a molybdenum or a rhodium filter.
The molybdenum anode, molybdenum filter system is quite good for general mammography in that it provides a spectrum that is very close to the optimum spectrum for smaller and less dense breasts. Many mammography machines give the operator the opportunity of selecting between molybdenum and rhodium filters, the latter being useful when imaging denser breasts.
Some systems have dual track anodes so that either molybdenum or rhodium can be selected as the anode material. Because of its higher atomic number (Z) rhodium produces characteristic x-radiation with higher energies than molybdenum. When the rhodium anode is selected, the beam penetration is increased. Generally, this produces better results when imaging dense breast. Since the physical parameters of X-ray sources used for mammography vary between different systems, a high variability is introduced between mammography images which is an artifact of the imaging parameters and not a result of different physiologies.
In order to assist radiologists in diagnosing breast cancer from mammography images, Computer Aided Detection (CAD) of suspicious findings has been introduced and is used at a growing number of clinical sites.
CAD systems for mammography, and indeed, for determining lung cancer as well, are based essentially on five basic processing steps:
(1) Segmentation of the organ to be analyzed;
(2) Location of tumor candidates;
(3) Extraction of the boundaries of tumor candidates;
(4) Extraction of feature parameters, and
(5) Discrimination between normal and abnormal features using classifiers.
Once a full CAD process has been engineered and trained using a certain set of mammograms, it is desirable that the same performance be obtained for other sets of mammograms. However, there are a great variety of systems in this field. For example, film mammography is still in wide usage, and digitizing a film gives images that look very different from the appearance of images generated by computerized mammography. The various Full field digital mammography (FFDM) systems produce images having different styles.
Similar performance is not obtained where CAD systems are used on digital mammograms of the same breasts if the mammograms are produced using totally different types of equipment. It has been verified, for example, that a CAD algorithm trained on a set of digitized film mammograms delivers poor performance on FFDM produced mammograms, even after these new images are modified to present the same basic characteristics of the digitized films.
Even within the same generic type of mammography equipment, each model has its own technical characteristics relating to among other things resolution, pixel depth (i.e. bits per pixel), dynamic range, and noise. Thus software trained on images obtained using one x-ray imaging system will not generally give good results when used with a different system. Some lesion candidates that should be classified as non relevant may be wrongly diagnosed as being malignant. Malignant lesions may be wrongly diagnosed as being non relevant.
Ideally each system improvement and each new apparatus should be trained using fully analyzed images. It is not always practical to do so however. Sometimes an appropriate image set is simply not available. There is thus a need to adapt software developed for one system to use on another system, and the need is particularly acute in the mammography field. The present invention addresses this need.